Passive acoustic sensor technology provides multiple benefits for detection and localization of various targets of interest that produce acoustic waves. Passive sensors rely on the target's own emissions, and, thus, do not have to emit any signals. Such passive sensors are covert, energy-efficient, and environmentally friendly. Any source that emits acoustic signals (i.e., an “acoustic sensor”) may be a target of interest. Examples of targets of interest in air can be various aircraft (e.g., small planes, helicopters, and ultra-light aircraft), Unmanned Aerial Vehicles (“UAV”), drones, and birds. Vehicles on ground surface, surface watercraft, and animals can also produce detectable acoustic waves that propagate in air. Acoustic waves generated by airborne sources can also excite ground vibrations that can be recorded by seismic sensors. For the purposes of the present disclosure, seismic sources and sensors are considered, generally, to be among the group of acoustic sources and sensors. Seismic sensors can be used for detection, tracking and classification of airborne and ground targets including vehicles, people, any machinery working on or touching the ground. The passive acoustic methods in water can be used for detection of submarines, boats, underwater vehicles and SCUBA divers and surface swimmers, fish and marine mammals. For the purposes of the present disclosure, such hydro-acoustic sources and sensors are also considered, generally, to be among the group of acoustic sources and sensors.
An acoustic sensor can include any transducer that converts acoustic waves into electrical signals. Typical acoustic sensors include microphones for acoustic waves in air, hydrophones for acoustic waves in water, and geophones for seismic waves. Numerous acoustic detection and tracking systems apply acoustic arrays consisting of many sensors. Such arrays are large and expensive. For example the length of a towing array for submarine detection by a Surveillance Towed Array Sensor System (SURTASS) is about 1.5 km.
Low cost acoustic sensors are used in devices such as Unattended Ground Sensors (UGS), which are used for personnel and vehicle detection and for battlefield surveillance. Acoustic target detection using single or multiple sensors is usually performed by detecting the level of acoustic signal exceeding a definite threshold. Acoustic target localization using several sensors is based on the determination of the Time Difference of Arrival (TDOA) for several sensors.
Various methods that determine the Time Difference of Arrival (TDOA) of an acoustic wave onto a pair of sensors are well known, along with efficient algorithms which may be used to compute TDOA. The TDOA estimate depends on the direction of arrival of the acoustic wave onto the pair of sensors. A minimal subset of pairs required to determine the direction of arrival is one pair for a two-dimensional case, where one can assume, a priori, that the target is in a certain plane, and two pairs for a three-dimensional case. Using the knowledge of the sensor geometry and the uncertainty of the TDOA estimate, one can determine the uncertainty of the direction-of-arrival measurement. An example of such a system is disclosed in U.S. Pat. No. 8,195,409, entitled PASSIVE ACOUSTIC UNDERWATER INTRUDER DETECTION SYSTEM, issued to Bruno, et al. on Jun. 5, 2012 and assigned to the assignee of the present application, the disclosure of which patent is hereby incorporated by reference for all purposes, and as if copied in the present application in full including all of the drawings and the claims.
Some of such algorithms used to determine direction or TDOA of an acoustic wave can provide only one estimate (for example, an estimate of TDOA for the signal source of the strongest signal, if there are several sources), while others are able to provide multiple estimates corresponding to several sources present simultaneously. For example, generalized cross-correlation algorithms produce peaks at the values of delays corresponding to TDOA of received signals from each of the sources, and, when combined with a peak detector, they can yield multiple TDOA estimates. Signals from multiple acoustic sources arriving onto a compactly-deployed acoustic sensor cluster may be separated by the direction of arrival.
If multiple estimates are produced from multiple pairs of sensors, and if those measurements must be considered simultaneously, a data association problem arises, as there is a need for an additional method to determine which of the TDOA estimates from one pair of sensors corresponds to the same wave that originated another estimate from another pair of sensors. In cases where the sensors are deployed in remote locations, it is a common problem that very limited resources are available (e.g., power, computational resources, communication bandwidth, or storage capacity). There is a need for a robust method to determine when the functionality consuming those resources is invoked.
A typical air acoustic wave sensor (e.g., a microphone) consists of a single element, such as an electret capsule, a ceramic element, etc. Such transducers can vary in cost depending on their properties, with high-end products providing high performance (in terms, e.g., of minimal self-noise and bandwidth sensitivity), but costing orders of magnitude more than lower-performance sensors. Such elements need additional electronics to supply power and pre-amplification. When connecting to devices responsible for processing signals, such sensors typically have to be supplemented with signal-conditioning electronics, such as multiple stages of amplifiers and filters.